A Universal Performance Measure

CFA Digest ◽  
2003 ◽  
Vol 33 (1) ◽  
pp. 51-52
Author(s):  
Frank T. Magiera
Keyword(s):  
Performance Measure

Work-Performance Measure

2011 ◽  
Author(s):  
Yih-teen Lee ◽  
Alfred Stettler ◽  
John Antonakis
Keyword(s):  
Work Performance ◽  
Performance Measure

Hotel Performance Measure

2019 ◽  
Author(s):  
Erick Pusck Wilke ◽  
Benny Kramer Costa ◽  
Otávio Bandeira De Lamônica Freire ◽  
Manuel Portugal Ferreira
Keyword(s):  
Performance Measure ◽  
Hotel Performance

GRAM: A True Null Model for Relative Binding Affinity Predictions

2019 ◽  
Author(s):  
Guanglei Cui ◽  
Alan P. Graves ◽  
Eric S. Manas
Keyword(s):  
Binding Affinity ◽  
Performance Metrics ◽  
Null Model ◽  
Performance Measure ◽  
Interval Estimate ◽  
Empirical Observation ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Relative Binding Affinity ◽  
Relative Binding

Relative binding affinity prediction is a critical component in computer aided drug design. Significant amount of effort has been dedicated to developing rapid and reliable in silico methods. However, robust assessment of their performance is still a complicated issue, as it requires a performance measure applicable in the prospective setting and more importantly a true null model that defines the expected performance of random in an objective manner. Although many performance metrics, such as correlation coefficient (r2), mean unsigned error (MUE), and room mean square error (RMSE), are frequently used in the literature, a true and non-trivial null model has yet been identified. To address this problem, here we introduce an interval estimate as an additional measure, namely prediction interval (PI), which can be estimated from the error distribution of the predictions. The benefits of using the interval estimate are 1) it provides the uncertainty range in the predicted activities, which is important in prospective applications; 2) a true null model with well-defined PI can be established. We provide one such example termed Gaussian Random Affinity Model (GRAM), which is based on the empirical observation that the affinity change in a typical lead optimization effort has the tendency to distribute normally N (0, s). Having an analytically defined PI that only depends on the variation in the activities, GRAM should in principle allow us to compare the performance of relative binding affinity prediction methods in a standard way, ultimately critical to measuring the progress made in algorithm development.<br>

Omega Performance Measure and Portfolio Insurance

2010 ◽  
Author(s):  
Philippe Bertrand ◽  
Jean-Luc Prigent
Keyword(s):  
Performance Measure ◽  
Portfolio Insurance

Investigating the Effect of Cognitive-behavioral Stress Management on Occupational Performance among Women with Breast Cancer

2020 ◽  
Vol 16 (2) ◽  
pp. 152-159
Author(s):  
Arezoo Shayan ◽  
Mansoureh Refaei ◽  
Farkhondeh Jamshidi
Keyword(s):  
Breast Cancer ◽  
Stress Management ◽  
Repeated Measures ◽  
Intervention Group ◽  
Performance Measure ◽  
Cognitive Behavioral ◽  
Occupational Performance ◽  
Cognitive Behavioral Stress Management ◽  
Behavioral Stress ◽  
Significant Difference

Background: Treatment of breast cancer can be accompanied by long-term consequences affecting women’s participation in many tasks. Objective: This study aimed to assess the effect of cognitive behavioral stress management (CBSM) program on occupational performance of women with breast cancer. Methods: In this randomized clinical trial, conducted between Feb 3 and Oct 26, 2016, 104 women with breast cancer who had referred to Imam Khomeini clinic in Hamadan, and who fulfilled the inclusion criteria (20-60 years old, married, suffering from grade 1-3 breast cancer with a history of recent surgery) were enrolled. They were randomly divided into two groups of 52 using a permuted block size of four. The intervention group took part in four 60-minute sessions of CBSM over four weeks. The study data were collected using a demographic information form and the Canadian Occupational Performance Measure. The statistical analyst was masked to intervention allocation. The data were analyzed using descriptive statistics, paired t-test, and repeated measures ANOVA. Results: A significant difference was observed between the two groups regarding the mean scores of occupational performance (p=0.02) and satisfaction (p=0.005) after the intervention. Each variable was measured at three time points (before the intervention, immediately and 2 weeks after intervention). A significant difference was observed in the two groups’ mean scores of performance (p=0.026) and satisfaction (p=0.01), irrespective of the time of assessment. Conclusion: The CBSM program promoted the occupational performance immediately and two weeks after the intervention in women with breast cancer. This technique can be used as a complementary method alongside medical therapies in oncology centers.

Multi-Index Conditional Investment Performance Measure: An Empirical Analysis

2016 ◽  
Vol 6 (3) ◽  
Author(s):  
Subrata Roy
Keyword(s):  
Empirical Analysis ◽  
Mutual Fund ◽  
Public Information ◽  
Fund Performance ◽  
Performance Measure ◽  
Mutual Fund Performance ◽  
Investment Performance ◽  
Multi Index ◽  
Time Lagged ◽  
Index Measure

The present study seeks to examine the mutual fund performance of the open-ended selected equity schemes of UTI based on multi-index measures as well as conditional multi-index measure. It is observed from the analysis that multi-index measure is able to capture the beta and alpha effects on market adjusted basis and the estimated coefficients is a better representative as compared to the single index measure. When time lagged (lagged at 1 month, 2 months, quarterly and yearly) multi-index measures are applied then the estimated coefficients (alpha & beta) which are market adjusted and time adjusted look more representative than the multi-index measure (without lagged effect). Finally, when we extended the time lagged multi-index measure on a conditional way (conditional on public information variables) then we observe that conditional multi-index lagged measure provides much more representative results in all respects as compared to the all measures after conditioning public information effects.

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